Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance
Leaf mass per area (LMA), leaf dry matter content (LDMC) and leaf water content/ equivalent water thickness (EWT) are commonly used functional plant traits in ecology. Whereas spectroscopy has recently proven to be a powerful tool to collect such functional trait information across large scales, it...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6c2476b9508446d4b299f8108e535e68 |
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| Sumario: | Leaf mass per area (LMA), leaf dry matter content (LDMC) and leaf water content/ equivalent water thickness (EWT) are commonly used functional plant traits in ecology. Whereas spectroscopy has recently proven to be a powerful tool to collect such functional trait information across large scales, it remains unclear whether these reflectance-based trait predictions are accurate enough to reliably model trait variation at the intraspecific level (i.e. across individuals of one species). We explored the potential of hyperspectral leaf reflectance-based methods to predict LMA, LDMC and EWT at the intraspecific level for two herbs (Hieracium umbellatum and Jacobaea vulgaris) and two shrubs (Rosa rugosa and Rubus caesius), based on 2400 leaf samples. More specifically we tested i) inversion of the PROSPECT-D radiative transfer model, ii) a generic PLSR approach using the multibiome LMA PLSR model and iii) a data-specific PLSR approach at the species level. For the latter approach we furthermore assessed both model transferability across species and the trade-off between sample size and model accuracy. Although the PROSPECT-D model inversion and the multibiome LMA PLSR model were relatively accurate for intraspecific LMA predictions of shrubs (R2 > 71 and 76%, respectively, however NRMSE = 33–47%), their performance was lower for herbs (R2 < 61%, NRMSE = 28–50%). PROSPECT-D was furthermore slightly less successful in retrieving EWT at the intraspecific level (R2 < 70%, NRMSE = 16–43%), and unsuccessful in retrieving LDMC through combining LMA and EWT inversion results (R2 < 10%, NRMSE = 9–192%). The highest correlation accuracy was obtained for all three traits with the species-specific PLSR models (R2 > 70%, NRMSE < 10%). If high predictive accuracy is needed, we thus suggest the use of species-specific PLSR models. The training of species-specific PLSR models comes at the cost of a needed sample size of 100–160 leaves however, depending on the trait. Although transferability of species-specific PLSR models seems limited overall, our results suggest potentially high transferability across herbaceous species. |
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